Li Liao and William Stafford Noble

Proceedings of the Sixth Annual International Conference on
Research in Computational Molecular Biology, April 18-21, 2002.
pp. 225-232.

Abstract

One key element in understanding the molecular machinery of the cell
is to understand the meaning, or function, of each protein encoded in
the genome. A very successful means of inferring the function of a
previously unannotated protein is via sequence similarity with one or
more proteins whose functions are already known. Currently, one of
the most powerful such homology detection methods is the SVM-Fisher
method of Jaakkola, Diekhans and Haussler (ISMB 2000). This method
combines a generative, profile hidden Markov model (HMM) with a
discriminative classification algorithm known as a support vector
machine (SVM). The current work presents an alternative method for
SVM-based protein classification. The method, SVM-pairwise, uses a
pairwise sequence similarity algorithm such as Smith-Waterman in place
of the HMM in the SVM-Fisher method. The resulting algorithm, when
tested on its ability to recognize previously unseen families from the
SCOP database, yields significantly better remote protein homology
detection than SVM-Fisher, profile HMMs and PSI-BLAST.
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